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SSJ V. 2.6. |
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java.lang.Object umontreal.iro.lecuyer.functionfit.LeastSquares
public class LeastSquares
This class implements different linear regression models, using the least squares method to estimate the regression coefficients. Given input data xij and response yi, one want to find the coefficients βj that minimize the residuals of the form (using matrix notation)
Sometimes, one wants to use a basis of general functions ψj(t) with a minimization of the form
Constructor Summary | |
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LeastSquares()
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Method Summary | |
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static double[] |
calcCoefficients(double[][] X,
double[] Y)
Computes the regression coefficients using the least squares method. |
static double[] |
calcCoefficients(double[] X,
double[] Y)
Computes the regression coefficients using the least squares method. |
static double[] |
calcCoefficients(double[] X,
double[] Y,
int deg)
Computes the regression coefficients using the least squares method. |
static double[] |
calcCoefficients0(double[][] X,
double[] Y)
Computes the regression coefficients using the least squares method. |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public LeastSquares()
Method Detail |
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public static double[] calcCoefficients(double[] X, double[] Y)
X
- the regressor variablesY
- the response
public static double[] calcCoefficients(double[] X, double[] Y, int deg)
X
- the regressor variablesY
- the response
public static double[] calcCoefficients0(double[][] X, double[] Y)
X
- the regressor variablesY
- the response
public static double[] calcCoefficients(double[][] X, double[] Y)
X
- the regressor variablesY
- the response
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SSJ V. 2.6. |
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PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |